de Cheveigné, A., and Kawahara, H. (1999). 
"Missing-data model of vowel identification,"  
JASA (accepted for publication)   
<p>
Vowel identity correlates well with the shape of the transfer function
of the vocal tract, in particular the position of the first two or
three formant peaks.  However in voiced speech the transfer function
is {\em sampled}\ at multiples of the fundamental frequency (\fo), and
the short-term spectrum contains peaks at those frequencies, rather
than at formants.  It is not clear how the auditory system estimates
the original spectral envelope from the vowel waveform.  Cochlear
excitation patterns, for example, resolve harmonics in the low
frequency region and their shape varies strongly with \fo.  The
problem cannot be cured by smoothing: lag-domain components of the
spectral envelope are aliased and cause \fo-dependent distortion.  The
problem is severe at high \fo s where the spectral envelope is
severely undersampled.  This paper treats vowel identification as a
process of pattern recognition with {\em missing data}.  Matching is
restricted to available data, and missing data are ignored using an
\fo-dependent weighting function that emphasizes regions near
harmonics.  The model is presented in two versions: a frequency-domain
version based on short-term spectra, or tonotopic excitation patterns,
and a time-domain version based on autocorrelation functions.  It
accounts for the relative \fo-independency observed in vowel
identification.